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ABSTRACT Our proposed decision trees using local support vector regression models ( t SVR, rt SVR) aim to efficiently handle the regression task for large datasets. The learning algorithm t SVR of regression models is done by two ...
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ABSTRACT Our proposed decision trees using local support vector regression models ( t SVR, rt SVR) aim to efficiently handle the regression task for large datasets. The learning algorithm t SVR of regression models is done by two main steps. The first one is to construct a decision tree regressor for partitioning the full training dataset into k terminal-nodes (subsets), followed which the second one is to learn the SVR model from each terminal-node to predict the data locally in a parallel way on multi-core computers. The algorithm rt SVR learns the random forest of decision trees with local SVR models for improving the prediction correctness against the t SVR model alone. The performance analysis shows that our algorithms t SVR, rt SVR are efficient in terms of the algorithmic complexity and the generalization ability compared to the classical SVR. The experimental results on five large datasets from UCI repository showed that proposed t SVR and rt SVR algorithms are faster than the standard SVR in training the non-linear regression model from large datasets while achieving the high correctness in the prediction. Typically, the average training time of t SVR and rt SVR are 1282.66 and 482.29 times faster than the standard SVR; Furthermore, t SVR and rt SVR improve 59.43%, 63.70% of the relative prediction correctness compared to the standard SVR.
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Support vector regression (SVR) is based on a linear combination of displaced replicas of the same function, called a kernel. When the function to be approximated is nonstationary, the single kernel approach may be ineffective, as...
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Support vector regression (SVR) is based on a linear combination of displaced replicas of the same function, called a kernel. When the function to be approximated is nonstationary, the single kernel approach may be ineffective, as it is not able to follow the variations in the frequency content in the different regions of the input space. The hierarchical support vector regression (HSVR) model presented here aims to provide a good solution also in these cases. HSVR consists of a set of hierarchical layers, each containing a standard SVR with Gaussian kernel at a given scale. Decreasing the scale layer by layer, details are incorporated inside the regression function. HSVR has been widely applied to noisy synthetic and real datasets and it has shown the ability in denoising the original data, obtaining an effective multiscale reconstruction of better quality than that obtained by standard SVR. Results also compare favorably with multikernel approaches. Furthermore, tuning the SVR configuration parameters is strongly simplified in the HSVR model.
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This paper proposes a new methodology, Sliding Window-based Support Vector Regression (SW-SVR), for micrometeorological data prediction. SVR is derived from a statistical learning theory and can be used to predict a quantity forwa...
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This paper proposes a new methodology, Sliding Window-based Support Vector Regression (SW-SVR), for micrometeorological data prediction. SVR is derived from a statistical learning theory and can be used to predict a quantity forward in time based on training that uses past data. Although SVR is superior to traditional learning algorithms such as Artificial Neural Network (ANN), it is difficult to choose the suitable amount of training data to build an optimum SVR model for micrometeorological data prediction. This paper revealed the periodic characteristics of micrometeorological data and evaluated SW-SVR can adapt the appropriate amount of training data to build an optimum SVR model automatically using parallel distributed processing. The future prediction experiment was conducted on air temperature of Sapporo, Tokyo, Hamamatsu, and Naha. As a result, SW-SVR has improved prediction accuracy in Sapporo, and Tokyo. In addition, it has reduced calculation time by more than 96% in all regions.
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In this paper we present a function to predict the survival of Lactobacillus acidophilus (LA) in concentrated yoghurt. For this purpose we used Artificial Intelligence tools based on Support Vector Machines for Regression (SVR). V...
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In this paper we present a function to predict the survival of Lactobacillus acidophilus (LA) in concentrated yoghurt. For this purpose we used Artificial Intelligence tools based on Support Vector Machines for Regression (SVR). Various parameters including: pH, percentage of prebiotic compounds (inulin and oligo-fructose) and inoculum dosage of probiotic bacteria which are effective factors on LA survival were considered. Performance of developed model was evaluated by calculating the mean square error (MSE). The results showed that the mean square error on days 1, 7, 14 and 21 were 1.04x10-5, 1.08x10-5, 9.56x10-6, 7.73x10- 6 respectively and defined model had the capacity of estimation accuracy for predicting survival of LA during storage in the refrigerator. Low values of MSE indicate that SVR is able to predict LA count in concentrated yoghurt.
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We propose and evaluate an empirical method for water depth determination from hyperspectral imagery when the benthic layer is visible using support vector regression (SVR). The implementation of the empirical method is presented,...
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We propose and evaluate an empirical method for water depth determination from hyperspectral imagery when the benthic layer is visible using support vector regression (SVR). The implementation of the empirical method is presented, and its ability to estimate water depths is compared with a more commonly used band ratio method for two distinct fluvial environments. Our analysis shows that SVR outperforms the band ratio method by providing better root-mean-square error (RMSE) agreement and higher for both clear and turbid water. We also demonstrate an extension of the nonparametric properties of SVR to provide estimates of water turbidity from hyperspectral imagery and show that the approach is able to estimate turbidity with an RMSE of approximately 1.2 NTU when compared with independent turbidity measurements.
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This paper aims to reveal the appropriate amount of training data for accurately and quickly building a support vector regression (SVR) model for micrometeorological data prediction. SVR is derived from statistical learning theory...
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This paper aims to reveal the appropriate amount of training data for accurately and quickly building a support vector regression (SVR) model for micrometeorological data prediction. SVR is derived from statistical learning theory and can be used to predict a quantity in the future based on training that uses past data. Although SVR is superior to traditional learning algorithms such as the artificial neural network (ANN), it is difficult to choose the most suitable amount of training data to build the appropriate SVR model for micrometeorological data prediction. The challenge of this paper is to reveal the periodic characteristics of micrometeorological data in Japan and determine the appropriate amount of training data to build the SVR model. By selecting the appropriate amount of training data, it is possible to improve both prediction accuracy and calculation time. When predicting air temperature in Sapporo, the prediction error was reduced by 0.1℃ and the calculation time was reduced by 98.7% using the appropriate amount of training data.
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In this paper we present a function to predict the carcass weight for beef cattle. The function uses a few zoometric measurements of the animals taken days before the slaughter. For this purpose we have used Artificial Intelligenc...
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In this paper we present a function to predict the carcass weight for beef cattle. The function uses a few zoometric measurements of the animals taken days before the slaughter. For this purpose we have used Artificial Intelligence tools based on Support Vector Machines for Regression (SVR). We report a case study done with a set of 390 measurements of 144 animals taken from 2 to 222days in advance of the slaughter. We used animals of the breed Asturiana de los Valles, a specialized beef breed from the North of Spain. The results obtained show that it is possible to predict carcass weights 150days before the slaughter day with an average absolute error of 4.27% of the true value. The prediction function is a polynomial of degree 3 that uses five lengths and the estimation of the round profile of the animals
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Sample data may be corrupted by noise in engineering problems. In order to make satisfactory approximations for the data with noise, some regression metamodels are adopted in current researches. The commonly used nugget-effect Kri...
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Sample data may be corrupted by noise in engineering problems. In order to make satisfactory approximations for the data with noise, some regression metamodels are adopted in current researches. The commonly used nugget-effect Kriging regards the variance of noise as a constant and ignores the difference of the noise influence, thus may not be effective enough in some cases. Therefore, a Kriging-based metamodel which combines the merits of Kriging and Support Vector Regression (SVR) is put forward for improving the performance in metamodeling with noisy data. The developed method, termed as SVEK, can capture the underlying trend of an unknown function efficiently by classifying the sample points and then regressing these classified points with different extents. Besides, a criterion for selecting the error margin epsilon in SVR training is proposed to facilitate the parameter setting process. Moreover, a one-variable test example is used to illustrate the modeling theory and construction procedures of SVEK. Eight numerical benchmark problems with different important characteristics are used to validate the proposed method. Then an overall comparison between the nugget-effect Kriging and the proposed method has been made. Results show that SVEK is promising in metamodeling with noisy data.
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Predicting network throughput is important for network-aware applications. Network throughput depends on a number of factors, and many throughput prediction methods have been proposed. However, many of these methods are suffering ...
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Predicting network throughput is important for network-aware applications. Network throughput depends on a number of factors, and many throughput prediction methods have been proposed. However, many of these methods are suffering from the fact that a distribution of traffic fluctuation is unclear and the scale and the bandwidth of networks are rapidly increasing. Furthermore, virtual machines are used as platforms in many network research and services fields, and they can affect network measurement. A prediction method that uses pairs of differently sized connections has been proposed. This method, which we call connection pair, features a small probe transfer using the TCP that can be used to predict the throughput of a large data transfer. We focus on measurements, analyses, and modeling for precise prediction results. We first clarified that the actual throughput for the connection pair is non-linearly and monotonically changed with noise. Second, we built a previously proposed predictor using the same training data sets as for our proposed method, and it was unsuitable for considering the above characteristics. We propose a throughput prediction method based on the connection pair that uses v-support vector regression and the polynomial kernel to deal with prediction models represented as a non-linear and continuous monotonic function. The prediction results of our method compared to those of the previous predictor are more accurate. Moreover, under an unstable network state, the drop in accuracy is also smaller than that of the previous predictor.
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In this paper we present a function to predict the survival of Lactobacillus acidophilus (LA) in concentrated yoghurt. For this purpose we used Artificial Intelligence tools based on Support Vector Machines for Regression (SVR). V...
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In this paper we present a function to predict the survival of Lactobacillus acidophilus (LA) in concentrated yoghurt. For this purpose we used Artificial Intelligence tools based on Support Vector Machines for Regression (SVR). Various parameters including: pH, percentage of prebiotic compounds (inulin and oligo-fructose) and inoculum dosage of probiotic bacteria which are effective factors on LA survival were considered. Performance of developed model was evaluated by calculating the mean square error (MSE). The results showed that the mean square error on days 1,7,14 and 21 were 1.04×10~(-5),1.08×10~(-5),9.56×10~(-6),7.73×10~(-6) respectively and defined model had the capacity of estimation accuracy for predicting survival of LA during storage in the refrigerator. Low values of MSE indicate that SVR is able to predict LA count in concentrated yoghurt.
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